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Community detection in node-attributed social networks: a survey. (English) Zbl 07349579
Summary: Community detection is a fundamental problem in social network analysis consisting, roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social graph) with certain social connections (modeled as edges in the social graph) into densely knitted and highly related groups with each group well separated from the others. Classical approaches for community detection usually deal only with the structure of the network and ignore features of the nodes (traditionally called node attributes), although the majority of real-world social networks provide additional actors’ information such as age, gender, interests, etc. It is believed that the attributes may clarify and enrich the knowledge about the actors and give sense to the detected communities. This belief has motivated the progress in developing community detection methods that use both the structure and the attributes of the network (modeled already via a node-attributed graph) to yield more informative and qualitative community detection results. During the last decade many such methods based on different ideas and techniques have appeared. Although there exist partial overviews of them, a recent survey is a necessity as the growing number of the methods may cause repetitions in methodology and uncertainty in practice. In this paper we aim at describing and clarifying the overall situation in the field of community detection in node-attributed social networks. Namely, we perform an exhaustive search of known methods and propose a classification of them based on when and how the structure and the attributes are fused. We not only give a description of each class but also provide general technical ideas behind each method in the class. Furthermore, we pay attention to available information which methods outperform others and which datasets and quality measures are used for their performance evaluation. Basing on the information collected, we make conclusions on the current state of the field and disclose several problems that seem important to be resolved in future.
MSC:
68-02 Research exposition (monographs, survey articles) pertaining to computer science
68-XX Computer science
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[1] A. A.damic, Lada; Adar, Eytan, Friends and neighbors on the web, Social Networks, 25, 3, 211-230 (2003)
[2] Adamic, Lada A.; Glance, Natalie, The political blogosphere and the 2004 U.S. election: Divided they blog, (Proceedings of the 3rd International Workshop on Link Discovery. Proceedings of the 3rd International Workshop on Link Discovery, LinkKDD ’05 (2005), ACM: ACM New York, NY, USA), 36-43
[3] Aggarwal, Charu C.; Zhai, ChengXiang, A survey of text clustering algorithms, (Mining Text Data (2012), Springer US: Springer US Boston, MA), 77-128
[4] Ahn, Yong-Yeol; Bagrow, James P.; Lehmann, Sune, Link communities reveal multiscale complexity in networks, Nature, 466, 761-764 (2010)
[5] Akbas, Esra; Zhao, Peixiang, Attributed graph clustering: An attribute-aware graph embedding approach, (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17 (2017), ACM: ACM New York, NY, USA), 305-308
[6] Akbas, Esra; Zhao, Peixiang, Graph clustering based on attribute-aware graph embedding, (From Security To Community Detection in Social Networking Platforms (2019), Springer International Publishing: Springer International Publishing Cham), 109-131
[7] Leman Akoglu, Hanghang Tong, Brendan Meeder, Christos Faloutsos, PICS: Parameter-free identification of cohesive subgroups in large attributed graphs, in: Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012, 2012, pp. 439-450.
[8] Alamsyah, Andry; Rahardjo, Budi; Kuspriyanto, Community detection methods in social network analysis, Adv. Sci. Lett., 20, 1, 250-253 (2014)
[9] Alinezhad, Esmaeil; Teimourpour, Babak; Sepehri, Mohammad Mehdi; Kargari, Mehrdad, Community detection in attributed networks considering both structural and attribute similarities: two mathematical programming approaches, Neural Comput. Appl. (2019)
[10] Ambroise, C.; Dang, M.; Govaert, G., Clustering of spatial data by the EM algorithm, (Soares, Amílcar; Gómez-Hernandez, Jaime; Froidevaux, Roland, GeoENV I — Geostatistics for Environmental Applications (1997), Springer Netherlands: Springer Netherlands Dordrecht), 493-504
[11] Asim, Yousra; Ghazal, Rubina; Naeem, Wajeeha; Majeed, Abdul; Raza, Basit; Malik, Ahmad Kamran, Community detection in networks using node attributes and modularity, Int. J. Adv. Comput. Sci. Appl., 8, 1 (2017)
[12] Atzmueller, Martin; Doerfel, Stephan; Mitzlaff, Folke, Description-oriented community detection using exhaustive subgroup discovery, Inform. Sci., 329, 965-984 (2016), Special issue on Discovery Science
[13] Atzmueller, Martin; Soldano, Henry; Santini, Guillaume; Bouthinon, Dominique, MinerLSD: efficient mining of local patterns on attributed networks, Appl. Netw. Sci., 4, 1, 43 (2019)
[14] Ramnath Balasubramanyan, William W. Cohen, Block-LDA: Jointly modeling entity-annotated text and entity-entity links, in: Proceedings of the 2011 SIAM International Conference on Data Mining, 2011, pp. 450-461.
[15] Baroni, Alessandro; Conte, Alessio; Patrignani, Maurizio; Ruggieri, Salvatore, Efficiently clustering very large attributed graphs, (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17 (2017), ACM: ACM New York, NY, USA), 369-376
[16] Benz, Dominik; Hotho, Andreas; Jäschke, Robert; Krause, Beate; Mitzlaff, Folke; Schmitz, Christoph; Stumme, Gerd, The social bookmark and publication management system bibsonomy, VLDB J., 19, 6, 849-875 (2010)
[17] M. Berlingerio, M. Coscia, F. Giannotti, Finding and characterizing communities in multidimensional networks, in: 2011 International Conference on Advances in Social Networks Analysis and Mining, 2011, pp. 490-494.
[18] Berlingerio, Michele; Pinelli, Fabio; Calabrese, Francesco, ABACUS: frequent pattern mining-based community discovery in multidimensional networks, Data Min. Knowl. Discov., 27, 3, 294-320 (2013) · Zbl 1281.68173
[19] Bhatt, Shreyansh; Padhee, Swati; Sheth, Amit; Chen, Keke; Shalin, Valerie; Doran, Derek; Minnery, Brandon, Knowledge graph enhanced community detection and characterization, (Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM ’19 (2019), ACM: ACM New York, NY, USA), 51-59
[20] Binkiewicz, N.; Vogelstein, J. T.; Rohe, K., Covariate-assisted spectral clustering, Biometrika, 104, 2, 361-377 (2017) · Zbl 07072212
[21] Blondel, Vincent D.; Guillaume, Jean-Loup; Lambiotte, Renaud; Lefebvre, Etienne, Fast unfolding of communities in large networks, J. Stat. Mech. Theory Exp., 2008, 10, P10008 (2008) · Zbl 1459.91130
[22] Boobalan, M. Parimala; Lopez, Daphne; Gao, X. Z., Graph clustering using k-neighbourhood attribute structural similarity, Appl. Soft Comput., 47, C, 216-223 (2016)
[23] Boongoen, Tossapon; Iam-On, Natthakan, Cluster ensembles: A survey of approaches with recent extensions and applications, Comp. Sci. Rev., 28, 1-25 (2018) · Zbl 1387.68195
[24] Bothorel, Cecile; Cruz, Juan David; Magnani, Matteo; Micenková, Barbora, Clustering attributed graphs: Models, measures and methods, Netw. Sci., 3, 3, 408-444 (2015)
[25] Boutemine, Oualid; Bouguessa, Mohamed, Mining community structures in multidimensional networks, ACM Trans. Knowl. Discov. Data, 11, 4, 51:1-51:36 (2017)
[26] Bu, Zhan; Gao, Guangliang; Li, Hui-Jia; Cao, Jie, CAMAS: A cluster-aware multiagent system for attributed graph clustering, Inf. Fusion, 37, 10-21 (2017)
[27] Bu, Z.; Li, H.; Cao, J.; Wang, Z.; Gao, G., Dynamic cluster formation game for attributed graph clustering, IEEE Trans. Cybern., 49, 1, 328-341 (2019)
[28] Cai, Deng; He, Xiaofei; Wu, Xiaoyun; Han, Jiawei, Non-negative matrix factorization on manifold, (Proceedings of the 2008 Eighth IEEE International Conference on Data Mining. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM ’08 (2008), IEEE Computer Society: IEEE Computer Society Washington, DC, USA), 63-72
[29] Cai, H.; Zheng, V. W.; Chang, K. C., A comprehensive survey of graph embedding: Problems, techniques, and applications, IEEE Trans. Knowl. Data Eng., 30, 9, 1616-1637 (2018)
[30] Cao, Xiangyong; Chang, Xiangyu; Xu, Zongben, Community detection for clustered attributed graphs via a variational EM algorithm, (Proceedings of the 2014 International Conference on Big Data Science and Computing. Proceedings of the 2014 International Conference on Big Data Science and Computing, BigDataScience ’14 (2014), ACM: ACM New York, NY, USA), 28:1
[31] Cao, Shaosheng; Lu, Wei; Xu, Qiongkai, Grarep: Learning graph representations with global structural information, (Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM ’15 (2015), ACM: ACM New York, NY, USA), 891-900
[32] Chai, Bian-fang; Yu, Jian; Jia, Cai-yan; Yang, Tian-bao; Jiang, Ya-wen, Combining a popularity-productivity stochastic block model with a discriminative-content model for general structure detection, Phys. Rev. E, 88, Article 012807 pp. (2013)
[33] Chakraborty, Tanmoy; Dalmia, Ayushi; Mukherjee, Animesh; Ganguly, Niloy, Metrics for community analysis: A survey, ACM Comput. Surv., 50, 4, 54:1-54:37 (2017)
[34] Chakraborty, Tanmoy; Srinivasan, Sriram; Ganguly, Niloy; Mukherjee, Animesh; Bhowmick, Sanjukta, On the permanence of vertices in network communities, (Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14 (2014), ACM: ACM New York, NY, USA), 1396-1405
[35] Chang, Jonathan; Blei, David, Relational topic models for document networks, (van Dyk, David; Welling, Max, Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, vol. 5 (2009), PMLR: PMLR Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA), 81-88
[36] Chang, Shiyu; Han, Wei; Tang, Jiliang; Qi, Guo-Jun; Aggarwal, Charu C.; Huang, Thomas S., Heterogeneous network embedding via deep architectures, (Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’15 (2015), ACM: ACM New York, NY, USA), 119-128
[37] Cheng, Chun-Hung; Fu, Ada Waichee; Zhang, Yi, Entropy-based subspace clustering for mining numerical data, (Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’99 (1999), ACM: ACM New York, NY, USA), 84-93
[38] Cheng, Hong; Zhou, Yang; Huang, Xin; Yu, Jeffrey Xu, Clustering large attributed information networks: an efficient incremental computing approach, Data Min. Knowl. Discov., 25, 3, 450-477 (2012) · Zbl 1259.05150
[39] Cheng, Hong; Zhou, Yang; Yu, Jeffrey Xu, Clustering large attributed graphs: A balance between structural and attribute similarities, ACM Trans. Knowl. Discov. Data, 5, 2, 12:1-12:33 (2011)
[40] Clauset, Aaron; Newman, M. E.J.; Moore, Cristopher, Finding community structure in very large networks, Phys. Rev. E, 70, Article 066111 pp. (2004)
[41] Cohn, David A.; Hofmann, Thomas, The missing link - a probabilistic model of document content and hypertext connectivity, (Leen, T. K.; Dietterich, T. G.; Tresp, V., Advances in Neural Information Processing Systems, vol. 13 (2001), MIT Press), 430-436
[42] Combe, David; Largeron, Christine; Egyed-Zsigmond, Elod; Gery, Mathias, Combining relations and text in scientific network clustering, (Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012. Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, ASONAM ’12 (2012), IEEE Computer Society: IEEE Computer Society Washington, DC, USA), 1248-1253
[43] Combe, David; Largeron, Christine; Géry, Mathias; Egyed-Zsigmond, Előd, I-louvain: An attributed graph clustering method, (Fromont, Elisa; De Bie, Tijl; van Leeuwen, Matthijs, Advances in Intelligent Data Analysis XIV (2015), Springer International Publishing: Springer International Publishing Cham), 181-192
[44] Coscia, Michele; Giannotti, Fosca; Pedreschi, Dino, A classification for community discovery methods in complex networks, Stat. Anal. Data Min., 4, 5, 512-546 (2011) · Zbl 07260300
[45] Craven, Mark; DiPasquo, Dan; Freitag, Dayne; McCallum, Andrew; Mitchell, Tom; Nigam, Kamal; Slattery, Sean, Learning to extract symbolic knowledge from the world wide web, (Proceedings of the Fifteenth National/Tenth Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence (1998), American Association for Artificial Intelligence: American Association for Artificial Intelligence Menlo Park), 509-516
[46] Cross, R.; Parker, A., The Hidden Power of Social Networks (2004), Harvard Business School Press: Harvard Business School Press Boston, MA, USA
[47] J.D. Cruz, C. Bothorel, Information integration for detecting communities in attributed graphs, in: 2013 Fifth International Conference on Computational Aspects of Social Networks, 2013, pp. 62-67.
[48] Cruz, Juan; Bothorel, Cécile; Poulet, François, Détection et visualisation des communautés dans les réseaux sociaux, Rev. Intell. Artif., 26, 369-392 (2012)
[49] Juan David Cruz Gomes, Cécile Bothorel, François Poulet, Semantic clustering of social networks using points of view, in: CORIA: Conférence en Recherche d’Information et Applications 2011, Avignon, France, 2011.
[50] Juan David Cruz Gomez, Cécile Bothorel, François Poulet, Entropy based community detection in augmented social networks, in: International Conference on Computational Aspects of Social Networks, Salamanca, Spain, 2011, pp. 163-168.
[51] Cui, P.; Wang, X.; Pei, J.; Zhu, W., A survey on network embedding, IEEE Trans. Knowl. Data Eng., 31, 5, 833-852 (2019)
[52] The Anh Dang, Emmanuel Viennet, Community detection based on structural and attribute similarities, in: International Conference on Digital Society, ICDS, Jan. 2012, pp. 7-14, (ISBN: 978-1-61208-176-2). Best paper award.
[53] Dempster, A. P.; Laird, N. M.; Rubin, D. B., Maximum likelihood from incomplete data via the EM algorithm, J. R. Stat. Soc. Ser. B Stat. Methodol., 39, 1, 1-38 (1977) · Zbl 0364.62022
[54] Descormiers, Karine; Morselli, Carlo, Alliances, conflicts, and contradictions in montreal’s street gang landscape, Int. Crim. Justice Rev., 21, 3, 297-314 (2011)
[55] Dhillon, Inderjit S.; Mallela, Subramanyam; Modha, Dharmendra S., Information-theoretic co-clustering, (Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’03 (2003), ACM: ACM New York, NY, USA), 89-98
[56] Ding, Ying, Community detection: Topological vs. topical, J. Inform., 5, 4, 498-514 (2011)
[57] Eagle, Nathan; Pentland, Alex (Sandy); Lazer, David, Inferring friendship network structure by using mobile phone data, Proc. Natl. Acad. Sci., 106, 36, 15274-15278 (2009)
[58] Elhadi, Haithum; Agam, Gady, Structure and attributes community detection: Comparative analysis of composite, ensemble and selection methods, (Proceedings of the 7th Workshop on Social Network Mining and Analysis. Proceedings of the 7th Workshop on Social Network Mining and Analysis, SNAKDD ’13 (2013), ACM: ACM New York, NY, USA), 10:1-10:7
[59] Erosheva, Elena; Fienberg, Stephen; Lafferty, John, Mixed-membership models of scientific publications, Proc. Natl. Acad. Sci., 101, Suppl. 1, 5220-5227 (2004)
[60] Ester, Martin; Ge, Rong; Gao, Byron J.; Hu, Zengjian; Ben-Moshe, Boaz, Joint cluster analysis of attribute data and relationship data: the connected k-center problem, (SDM (2006)) · Zbl 1156.68565
[61] Issam Falih, Nistor Grozavu, Rushed Kanawati, Younes Bennani, Community detection in attributed network, in: WWW ’18 Companion Proceedings of the the Web Conference 2018, 2018, pp. 1299-1306.
[62] Falih, Issam; Grozavu, Nistor; Kanawati, Rushed; Bennani, Younès, ANCA : Attributed network clustering algorithm, (Cherifi, Chantal; Cherifi, Hocine; Karsai, Márton; Musolesi, Mirco, Complex Networks & their Applications VI (2018), Springer International Publishing: Springer International Publishing Cham), 241-252
[63] Farzi, Saeed; Kianian, Sahar, A novel clustering algorithm for attributed graphs based on k-medoid algorithm, J. Exp. Theor. Artif. Intell., 30, 6, 795-809 (2018)
[64] Andrew Fiore, Judith Donath, Homophily in online dating: When do you like someone like yourself? in: Conference on Human Factors in Computing Systems - Proceedings, 2005, pp. 1371-1374.
[65] Fortunato, Santo, Community detection in graphs, Phys. Rep., 486, 3, 75-174 (2010)
[66] Fred, A. L.N.; Jain, A. K., Data clustering using evidence accumulation, (Object Recognition Supported By User Interaction for Service Robots, vol. 4 (2002)), 276-280
[67] Frey, Brendan J.; Dueck, Delbert, Clustering by passing messages between data points, Science, 315, 5814, 972-976 (2007) · Zbl 1226.94027
[68] Gao, Hongchang; Huang, Heng, Deep attributed network embedding, (Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18 (2018), International Joint Conferences on Artificial Intelligence Organization), 3364-3370
[69] Gao, Jing; Liang, Feng; Fan, Wei; Wang, Chi; Sun, Yizhou; Han, Jiawei, On community outliers and their efficient detection in information networks, (Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’10 (2010), ACM: ACM New York, NY, USA), 813-822
[70] Ge, Rong; Ester, Martin; Gao, Byron J.; Hu, Zengjian; Bhattacharya, Binay; Ben-Moshe, Boaz, Joint cluster analysis of attribute data and relationship data: The connected k-center problem, algorithms and applications, ACM Trans. Knowl. Discov. Data, 2, 2, 7:1-7:35 (2008)
[71] Getoor, Lisa; Friedman, Nir; Koller, Daphne; Taskar, Benjamin, Learning probabilistic models of link structure, J. Mach. Learn. Res., 3, 679-707 (2003) · Zbl 1112.68441
[72] Girvan, M.; Newman, M. E.J., Community structure in social and biological networks, Proc. Natl. Acad. Sci., 99, 12, 7821-7826 (2002) · Zbl 1032.91716
[73] Greene, Derek; Cunningham, Pádraig, Producing a unified graph representation from multiple social network views, (Proceedings of the 5th Annual ACM Web Science Conference. Proceedings of the 5th Annual ACM Web Science Conference, WebSci ’13 (2013), ACM: ACM New York, NY, USA), 118-121
[74] Grover, Aditya; Leskovec, Jure, Node2vec: Scalable feature learning for networks, (Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16 (2016), ACM: ACM New York, NY, USA), 855-864
[75] Grund, Thomas U.; Densley, James A., Ethnic homophily and triad closure: Mapping internal gang structure using exponential random graph models, J. Contemp. Crim. Justice, 31, 3, 354-370 (2015)
[76] Grünwald, P. D., The Minimum Description Length Principle (2007), The MIT Press
[77] Gu, Quanquan; Zhou, Jie, Co-clustering on manifolds, (Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’09 (2009), ACM: ACM New York, NY, USA), 359-368
[78] Gullo, Francesco; Domeniconi, Carlotta; Tagarelli, Andrea, Projective clustering ensembles, Data Min. Knowl. Discov., 26, 3, 452-511 (2013) · Zbl 1267.68181
[79] Gunnemann, Stephan, Subspace clustering for complex data, (Markl, Volker; Saake, Gunter; Sattler, Kai-Uwe; Hackenbroich, Gregor; Mitschang, Bernhard; Harder, Theo; Koppen, Veit, Datenbanksysteme Fur Business, Technologie Und Web (BTW) 2034 (2013), Gesellschaft fur Informatik e.V.: Gesellschaft fur Informatik e.V. Bonn), 343-362
[80] Günnemann, Stephan; Boden, Brigitte; Färber, Ines; Seidl, Thomas, Efficient mining of combined subspace and subgraph clusters in graphs with feature vectors, (Pei, Jian; Tseng, Vincent S.; Cao, Longbing; Motoda, Hiroshi; Xu, Guandong, Advances in Knowledge Discovery and Data Mining (2013), Springer Berlin Heidelberg: Springer Berlin Heidelberg Berlin, Heidelberg), 261-275
[81] Günnemann, Stephan; Boden, Brigitte; Seidl, Thomas, DB-CSC: A density-based approach for subspace clustering in graphs with feature vectors, (Proceedings of the 2011th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I. Proceedings of the 2011th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I, ECMLPKDD’11 (2011), Springer-Verlag: Springer-Verlag Berlin, Heidelberg), 565-580
[82] S. Gunnemann, I. Farber, B. Boden, T. Seidl, Subspace clustering meets dense subgraph mining: A synthesis of two paradigms, in: 2010 IEEE International Conference on Data Mining, 2010, pp. 845-850.
[83] Günnemann, Stephan; Färber, Ines; Boden, Brigitte; Seidl, Thomas, GAMer: a synthesis of subspace clustering and dense subgraph mining, Knowl. Inf. Syst., 40, 2, 243-278 (2014)
[84] Günnemann, Stephan; Färber, Ines; Raubach, Sebastian; Seidl, Thomas, Spectral subspace clustering for graphs with feature vectors, (2013 IEEE 13th International Conference on Data Mining (2013)), 231-240
[85] Guo, T.; Pan, S.; Zhu, X.; Zhang, C., CFOND: Consensus factorization for co-clustering networked data, IEEE Trans. Knowl. Data Eng., 31, 4, 706-719 (2019)
[86] Hamilton, Will; Ying, Zhitao; Leskovec, Jure, Inductive representation learning on large graphs, (Guyon, I.; Luxburg, U. V.; Bengio, S.; Wallach, H.; Fergus, R.; Vishwanathan, S.; Garnett, R., Advances in Neural Information Processing Systems, vol. 30 (2017), Curran Associates, Inc.), 1024-1034
[87] Hartigan, J. A.; Wong, M. A., A k-means clustering algorithm, J. R. Stat. Soc. Ser. C. Appl. Stat., 28, 1, 100-108 (1979) · Zbl 0447.62062
[88] C. He, X. Fei, H. Li, Y. Tang, H. Liu, Q. Chen, A multi-view clustering method for community discovery integrating links and tags, in: 2017 IEEE 14th International Conference on E-Business Engineering, ICEBE, 2017, pp. 23-30.
[89] He, Dongxiao; Feng, Zhiyong; Jin, Di; Wang, Xiaobao; Zhang, Weixiong, Joint identification of network communities and semantics via integrative modeling of network topologies and node contents, (Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI’17 (2017), AAAI Press), 116-124
[90] He, Chaobo; Liu, Shuangyin; Zhang, Lei; Zheng, Jianhua, A fuzzy clustering based method for attributed graph partitioning, J. Ambient Intell. Humanized Comput., 10, 9, 3399-3407 (2019)
[91] Holland, Paul W.; Laskey, Kathryn Blackmond; Leinhardt, Samuel, Stochastic blockmodels: First steps, Social Networks, 5, 2, 109-137 (1983)
[92] Hric, Darko; Darst, Richard K.; Fortunato, Santo, Community detection in networks: Structural communities versus ground truth, Phys. Rev. E, 90, Article 062805 pp. (2014)
[93] Hu, L.; Chan, K. C.C., Fuzzy clustering in a complex network based on content relevance and link structures, IEEE Trans. Fuzzy Syst., 24, 2, 456-470 (2016)
[94] Huang, Xiao; Li, Jundong; Hu, Xia, Label informed attributed network embedding, (Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM ’17 (2017), ACM: ACM New York, NY, USA), 731-739
[95] Huang, Zhipeng; Mamoulis, Nikos, Heterogeneous information network embedding for meta path based proximity (2017), arXiv arXiv:abs/1701.05291
[96] Huang, Bingyang; Wang, Chaokun; Wang, Binbin, NMLPA: Uncovering overlapping communities in attributed networks via a multi-label propagation approach, Sensors (Basel, Switzerland), 19, 2, 260 (2019)
[97] Y. Huang, H. Wangg, Consensus and multiplex approach for community detection in attributed networks, in: 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP, 2016, pp. 425-429.
[98] Huang, Zhichao; Ye, Yunming; Li, Xutao; Liu, Feng; Chen, Huajie, Joint weighted nonnegative matrix factorization for mining attributed graphs, (Kim, Jinho; Shim, Kyuseok; Cao, Longbing; Lee, Jae-Gil; Lin, Xuemin; Moon, Yang-Sae, Advances in Knowledge Discovery and Data Mining (2017), Springer International Publishing: Springer International Publishing Cham), 368-380
[99] Interdonato, Roberto; Atzmueller, Martin; Gaito, Sabrina; Kanawati, Rushed; Largeron, Christine; Sala, Alessandra, Feature-rich networks: going beyond complex network topologies, Appl. Netw. Sci., 4, 1, 4 (2019)
[100] Hiroyoshi Ito, Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa, Community detection and correlated attribute cluster analysis on multi-attributed graphs, in: EDBT/ICDT Workshops, 2018.
[101] Iwata, Tomoharu; Saito, Kazumi; Ueda, Naonori; Stromsten, Sean; Griffiths, Thomas L.; Tenenbaum, Joshua B., Parametric embedding for class visualization, Neural Comput., 19, 9, 2536-2556 (2007) · Zbl 1143.68543
[102] Jia, Caiyan; Li, Yafang; Carson, Matthew B.; Wang, Xiaoyang; Yu, Jian, Node attribute-enhanced community detection in complex networks, Sci. Rep., 7:2626, 1-15 (2017)
[103] Jianbo Shi; Malik, J., Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 22, 8, 888-905 (2000)
[104] Johnson, Stephen C., Hierarchical clustering schemes, Psychometrika, 32, 3, 241-254 (1967) · Zbl 1367.62191
[105] D.R. Karger, Global min-cuts in RNC, and other ramifications of a simple min-cut algorithm, in: Proc. 4th Annual ACM-SIAM Symposium on Discrete Algorithms, 1993, pp. 21-30. · Zbl 0801.68124
[106] Karypis, George; Kumar, Vipin, A fast and high quality multilevel scheme for partitioning irregular graphs, SIAM J. Sci. Comput., 20, 1, 359-392 (1998) · Zbl 0915.68129
[107] N. Khediri, W. Karoui, Community detection in social network with node attributes based on formal concept analysis, in: 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications, AICCSA, Oct. 2017, pp. 1346-1353.
[108] Kipf, Thomas N.; Welling, Max, Variational graph auto-encoders (2016), arXiv arXiv:abs/1611.07308
[109] Kivelä, Mikko; Arenas, Alex; Barthelemy, Marc; Gleeson, James P.; Moreno, Yamir; Porter, Mason A., Multilayer networks, J. Complex Netw., 2, 3, 203-271 (2014)
[110] Kossinets, Gueorgi; Watts, Duncan J., Origins of homophily in an evolving social network, Am. J. Sociol., 115, 405-450 (2009)
[111] Lancichinetti, Andrea; Fortunato, Santo, Consensus clustering in complex networks, Sci. Rep., 2:336, 1-7 (2012)
[112] Lancichinetti, Andrea; Fortunato, Santo; Kertész, János, Detecting the overlapping and hierarchical community structure in complex networks, New J. Phys., 11, 3, Article 033015 pp. (2009)
[113] Lancichinetti, Andrea; Fortunato, Santo; Radicchi, Filippo, Benchmark graphs for testing community detection algorithms, Phys. Rev. E, 78, Article 046110 pp. (2008)
[114] Lazega, E., The Collegial Phenomenon: The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership (2001), Oxford University Press: Oxford University Press Oxford, UK
[115] T.M. V. Le, H. W. Lauw, Probabilistic latent document network embedding, in: 2014 IEEE International Conference on Data Mining, 2014, pp. 270-279.
[116] Lee, Daniel D.; Seung, H. Sebastian, Algorithms for non-negative matrix factorization, (Leen, T. K.; Dietterich, T. G.; Tresp, V., Advances in Neural Information Processing Systems, vol. 13 (2001), MIT Press), 556-562
[117] Leskovec, Jure; Lang, Kevin J.; Mahoney, Michael, Empirical comparison of algorithms for network community detection, (Proceedings of the 19th International Conference on World Wide Web. Proceedings of the 19th International Conference on World Wide Web, WWW ’10 (2010), ACM: ACM New York, NY, USA), 631-640
[118] Leskovec, Jure; Mcauley, Julian J., Learning to discover social circles in ego networks, (Pereira, F.; Burges, C. J.C.; Bottou, L.; Weinberger, K. Q., Advances in Neural Information Processing Systems, vol. 25 (2012), Curran Associates, Inc.), 539-547
[119] Li, Jundong; Guo, Ruocheng; Liu, Chenghao; Liu, Huan, Adaptive unsupervised feature selection on attributed networks, (Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’19 (2019), Association for Computing Machinery: Association for Computing Machinery New York, NY, USA), 92-100
[120] Li, P.; Huang, L.; Wang, C.; Huang, D.; Lai, J., Community detection using attribute homogenous motif, IEEE Access, 6, 47707-47716 (2018)
[121] Li, Y.; Jia, C.; Kong, X.; Yang, L.; Yu, J., Locally weighted fusion of structural and attribute information in graph clustering, IEEE Trans. Cybern., 49, 1, 247-260 (2019)
[122] Li, Z.; Liu, J.; Wu, K., A multiobjective evolutionary algorithm based on structural and attribute similarities for community detection in attributed networks, IEEE Trans. Cybern., 48, 7, 1963-1976 (2018)
[123] Li, Zhen; Pan, Zhisong; Hu, Guyu; Li, Guopeng; Zhou, Xingyu, Detecting semantic communities in social networks, IEICE Trans. Fundam. Electron. Commun. Comput. Sci., E100.A, 11, 2507-2512 (2017)
[124] Li, Ye; Sha, Chaofeng; Huang, Xin; Zhang, Yanchun, Community detection in attributed graphs: An embedding approach, (AAAI (2018))
[125] Li, Wu-Jun; Yeung, Dit-Yan; Zhang, Zhihua, Generalized latent factor models for social network analysis, (IJCAI (2011))
[126] L. Liu, L. Xu, Z. Wangy, E. Chen, Community detection based on structure and content: A content propagation perspective, in: 2015 IEEE International Conference on Data Mining, Nov. 2015, pp. 271-280.
[127] Luo, Sheng; Zhang, Zhifei; Zhang, Yuanjian; Ma, Shuwen, Co-association matrix-based multi-layer fusion for community detection in attributed networks, Entropy, 21, 1 (2019)
[128] Madey, Gregory R.; Barabási, Albert-László; Chawla, Nitesh V.; Gonzalez, Marta; Hachen, David; Lantz, Brett; Pawling, Alec; Schoenharl, Timothy; Szabó, Gábor; Wang, Pu; Yan, Ping, Enhanced situational awareness: Application of DDDAS concepts to emergency and disaster management, (Shi, Yong; van Albada, Geert Dick; Dongarra, Jack; Sloot, Peter M. A., Computational Science - ICCS 2007 (2007), Springer Berlin Heidelberg: Springer Berlin Heidelberg Berlin, Heidelberg), 1090-1097
[129] Marsden, Peter V.; Friedkin, Noah E., Network studies of social influence, Sociol. Methods Res., 22, 1, 127-151 (1993)
[130] Mcauley, Julian; Leskovec, Jure, Discovering social circles in ego networks, ACM Trans. Knowl. Discov. Data, 8, 1, 4:1-4:28 (2014)
[131] McPherson, Miller; Smith-Lovin, Lynn; Cook, James M., Birds of a feather: Homophily in social networks, Annu. Rev. Sociol., 27, 1, 415-444 (2001)
[132] Meng, Fanrong; Rui, Xiaobin; Wang, Zhixiao; Xing, Yan; Cao, Longbing, Coupled node similarity learning for community detection in attributed networks, Entropy, 20, 6 (2018)
[133] Milo, R.; Shen-Orr, S.; Itzkovitz, S.; Kashtan, N.; Chklovskii, D.; Alon, U., Network motifs: Simple building blocks of complex networks, Science, 298, 5594, 824-827 (2002)
[134] Mislove, Alan; Marcon, Massimiliano; Gummadi, Krishna P.; Druschel, Peter; Bhattacharjee, Bobby, Measurement and analysis of online social networks, (Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement. Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, IMC ’07 (2007), ACM: ACM New York, NY, USA), 29-42
[135] Moser, Flavia; Colak, Recep; Rafiey, Arash; Ester, Martin, Mining cohesive patterns from graphs with feature vectors, (SDM (2009), SIAM), 593-604
[136] Moser, Flavia; Ge, Rong; Ester, Martin, Joint cluster analysis of attribute and relationship data withouta-priori specification of the number of clusters, (Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’07 (2007), ACM: ACM New York, NY, USA), 510-519
[137] Muller, E.; Sanchez, P. I.; Mulle, Y.; Bohm, K., Ranking outlier nodes in subspaces of attributed graphs, (2013 IEEE 29th International Conference on Data Engineering Workshops, ICDEW 2013 (2013), IEEE Computer Society: IEEE Computer Society Los Alamitos, CA, USA), 216-222
[138] Muslim, N., A combination approach to community detection in social networks by utilizing structural and attribute data, Soc. Network., 5, 11-15 (2016)
[139] M. P. Naik, H. B. Prajapati, V. K. Dabhi, A survey on semantic document clustering, in: 2015 IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT, 2015, pp. 1-10.
[140] Nallapati, Ramesh M.; Ahmed, Amr; Xing, Eric P.; Cohen, William W., Joint latent topic models for text and citations, (Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’08 (2008), ACM: ACM New York, NY, USA), 542-550
[141] Nawaz, Waqas; Khan, Kifayat-Ullah; Lee, Young-Koo; Lee, Sungyoung, Intra graph clustering using collaborative similarity measure, Distrib. Parallel Databases, 33, 4, 583-603 (2015)
[142] Jennifer Neville, Micah Adler, David Jensen, Clustering relational data using attribute and link information, in: Proceedings of the Text Mining and Link Analysis Workshop, 18th International Joint Conference on Artificial Intelligence, 2003, pp. 9-15.
[143] Newman, M. E.J., Finding community structure in networks using the eigenvectors of matrices, Phys. Rev. E, 74, Article 036104 pp. (2006)
[144] Newman, M.; Clauset, Aaron, Structure and inference in annotated networks, Nature Commun., 7 (2015)
[145] Newman, M. E.J.; Girvan, M., Finding and evaluating community structure in networks, Phys. Rev. E, 69, Article 026113 pp. (2004)
[146] H.T. Nguyen, T.N. Dinh, Unveiling the structure of multi-attributed networks via joint non-negative matrix factorization, in: MILCOM 2015 - 2015 IEEE Military Communications Conference, Oct. 2015, pp. 1379-1384.
[147] Nooy, Wouter de; Mrvar, Andrej; Batagelj, Vladimir, Exploratory Social Network Analysis with Pajek (2004), Cambridge University Press: Cambridge University Press New York, NY, USA
[148] Madalina Olteanu, Nathalie Villa-Vialaneix, Christine Cierco-Ayrolles, Multiple kernel self-organizing maps, in: Verleysen, M. (Ed.), European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 2013, p. 83.
[149] Papadopoulos, Andreas; Pallis, George; Dikaiakos, Marios D., Weighted clustering of attributed multi-graphs, Computing, 99, 9, 813-840 (2017) · Zbl 1380.05095
[150] Papadopoulos, Andreas; Rafailidis, Dimitrios; Pallis, George; Dikaiakos, Marios D., Clustering attributed multi-graphs with information ranking, (Chen, Qiming; Hameurlain, Abdelkader; Toumani, Farouk; Wagner, Roland; Decker, Hendrik, Database and Expert Systems Applications (2015), Springer International Publishing: Springer International Publishing Cham), 432-446
[151] M. Parimala, Daphne Lopez, Graph clustering based on Structural Attribute Neighborhood Similarity (SANS), in: 2015 IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT, 2015, pp. 1-4.
[152] Peel, Leto; Larremore, Daniel B.; Clauset, Aaron, The ground truth about metadata and community detection in networks, Sci. Adv., 3, 5 (2017)
[153] Pei, Yulong; Chakraborty, Nilanjan; Sycara, Katia, Nonnegative matrix tri-factorization with graph regularization for community detection in social networks, (Proceedings of the 24th International Conference on Artificial Intelligence. Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI’15 (2015), AAAI Press), 2083-2089
[154] Z. Pei, X. Zhang, F. Zhang, B. Fang, Attributed multi-layer network embedding, in: 2018 IEEE International Conference on Big Data, Big Data, Dec. 2018, pp. 3701-3710.
[155] Perozzi, Bryan; Akoglu, Leman; Iglesias Sánchez, Patricia; Müller, Emmanuel, Focused clustering and outlier detection in large attributed graphs, (Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14 (2014), ACM: ACM New York, NY, USA), 1346-1355
[156] Pizzuti, Clara; Socievole, Annalisa, A genetic algorithm for community detection in attributed graphs, (Sim, Kevin; Kaufmann, Paul, Applications of Evolutionary Computation (2018), Springer International Publishing: Springer International Publishing Cham), 159-170
[157] Pizzuti, C.; Socievole, A., Multiobjective optimization and local merge for clustering attributed graphs, IEEE Trans. Cybern., 1-13 (2019)
[158] Pool, Simon; Bonchi, Francesco; Leeuwen, Matthijs van, Description-driven community detection, ACM Trans. Intell. Syst. Technol., 5, 2, 28:1-28:28 (2014)
[159] Qin, Meng; Jin, Di; Lei, Kai; Gabrys, Bogdan; Musial-Gabrys, Katarzyna, Adaptive community detection incorporating topology and content in social networks, Knowl.-Based Syst., 161, 342-356 (2018)
[160] Ruan, Yiye; Fuhry, David; Parthasarathy, Srinivasan, Efficient community detection in large networks using content and links, (Proceedings of the 22nd International Conference on World Wide Web (2013), ACM: ACM New York, NY, USA), 1089-1098
[161] Sachan, Mrinmaya; Contractor, Danish; Faruquie, Tanveer A.; Subramaniam, L. Venkata, Using content and interactions for discovering communities in social networks, (Proceedings of the 21st International Conference on World Wide Web. Proceedings of the 21st International Conference on World Wide Web, WWW ’12 (2012), ACM: ACM New York, NY, USA), 331-340
[162] N. Y. Saiyad, H. B. Prajapati, V. K. Dabhi, A survey of document clustering using semantic approach, in: 2016 International Conference on Electrical, Electronics, and Optimization Techniques, ICEEOT, 2016, pp. 2555-2562.
[163] Sánchez, Patricia Iglesias; Müller, Emmanuel; Korn, Uwe Leo; Böhm, Klemens; Kappes, Andrea; Hartmann, Tanja; Wagner, Dorothea, Efficient algorithms for a robust modularity-driven clustering of attributed graphs, (SDM (2015))
[164] P. I. Sanchez, E. Muller, F. Laforet, F. Keller, K. Bohm, Statistical selection of congruent subspaces for mining attributed graphs, in: 2013 IEEE 13th International Conference on Data Mining, 2013, pp. 647-656.
[165] Satuluri, Venu; Parthasarathy, Srinivasan, Scalable graph clustering using stochastic flows: Applications to community discovery, (Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2009), ACM: ACM New York, NY, USA), 737-746
[166] Schaeffer, Satu Elisa, Graph clustering, Comp. Sci. Rev., 1, 1, 27-64 (2007) · Zbl 1302.68237
[167] Sen, Prithviraj; Namata, Galileo; Bilgic, Mustafa; Getoor, Lise; Gallagher, Brian; Eliassi-Rad, Tina, Collective classifiction in network data, AI Mag., 29, 93-106 (2008)
[168] Sheikh, Nasrullah; Kefato, Zekarias; Montresor, Alberto, Gat2vec: representation learning for attributed graphs, Computing, 101, 3, 187-209 (2019)
[169] Shiga, Motoki; Takigawa, Ichigaku; Mamitsuka, Hiroshi, A spectral clustering approach to optimally combining numericalvectors with a modular network, (Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’07 (2007), ACM: ACM New York, NY, USA), 647-656
[170] Snijders, Tom A. B.; Nowicki, Krzysztof, Estimation and prediction for stochastic blockmodels for graphs with latent block structure, J. Classification, 14, 75-100 (1997) · Zbl 0896.62063
[171] Stein, Benno; Niggemann, Oliver, On the nature of structure and its identification, (Proceedings of the 25th International Workshop on Graph-Theoretic Concepts in Computer Science. Proceedings of the 25th International Workshop on Graph-Theoretic Concepts in Computer Science, WG ’99 (1999), Springer-Verlag: Springer-Verlag London, UK, UK), 122-134
[172] Steinhaeuser, Karsten; Chawla, Nitesh V., Community detection in a large real-world social network, (Liu, Huan; Salerno, John J.; Young, Michael J., Social Computing, Behavioral Modeling, and Prediction (2008), Springer US: Springer US Boston, MA), 168-175
[173] Steinhaeuser, Karsten; Chawla, Nitesh V., Identifying and evaluating community structure in complex networks, Pattern Recognit. Lett., 31, 5, 413-421 (2010)
[174] Strehl, Alexander; Ghosh, Joydeep, Cluster ensembles — a knowledge reuse framework for combining multiple partitions, J. Mach. Learn. Res., 3, 583-617 (2003) · Zbl 1084.68759
[175] Y. Sun, J. Han, J. Gao, Y. Yu, iTopicModel: Information network-integrated topic modeling, in: 2009 Ninth IEEE International Conference on Data Mining, 2009, pp. 493-502.
[176] Tagarelli, Andrea; Amelio, Alessia; Gullo, Francesco, Ensemble-based community detection in multilayer networks, Data Min. Knowl. Discov., 31, 5, 1506-1543 (2017) · Zbl 1411.68122
[177] Tandon, Aditya; Albeshri, Aiiad; Thayananthan, Vijey; Alhalabi, Wadee; Fortunato, Santo, Fast consensus clustering in complex networks, Phys. Rev. E, 99, Article 042301 pp. (2019)
[178] Tang, Lei; Liu, Huan, Scalable learning of collective behavior based on sparse social dimensions, (Proceedings of the 18th ACM Conference on Information and Knowledge Management. Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM ’09 (2009), ACM: ACM New York, NY, USA), 1107-1116
[179] Tang, Jian; Qu, Meng; Wang, Mingzhe; Zhang, Ming; Yan, Jun; Mei, Qiaozhu, LINE: Large-scale information network embedding, (Proceedings of the 24th International Conference on World Wide Web. Proceedings of the 24th International Conference on World Wide Web, WWW ’15 (2015), International World Wide Web Conferences Steering Committee: International World Wide Web Conferences Steering Committee Republic and Canton of Geneva, Switzerland), 1067-1077
[180] Tepper, Mariano; Sapiro, Guillermo, From local to global communities in large networks through consensus, (Pardo, Alvaro; Kittler, Josef, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (2015), Springer International Publishing: Springer International Publishing Cham), 659-666
[181] Tian, Fei; Gao, Bin; Cui, Qing; Chen, Enhong; Liu, Tie-Yan, Learning deep representations for graph clustering, (Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, AAAI’14 (2014), AAAI Press), 1293-1299
[182] Tian, Yuanyuan; Hankins, Richard A.; Patel, Jignesh M., Efficient aggregation for graph summarization, (Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD ’08 (2008), ACM: ACM New York, NY, USA), 567-580
[183] H. Tong, C. Faloutsos, J. Pan, Fast random walk with restart and its applications, in: Sixth International Conference on Data Mining, ICDM’06, 2006, pp. 613-622.
[184] Traud, A. L.; Kelsic, E. D.; Mucha, P. J.; Porter, M. A., Comparing community structure to characteristics in online collegiate social networks, SIAM Rev., 53, 3, 526-543 (2011)
[185] Traud, Amanda L.; Mucha, Peter J.; Porter, Mason A., Social structure of facebook networks, Physica A, 391, 16, 4165-4180 (2012)
[186] Nathalie Villa-Vialaneix, Madalina Olteanu, Christine Cierco-Ayrolles, Carte auto-organisatrice pour graphes étiquetés, in: Atelier Fouilles de Grands Graphes (FGG) - EGC’2013, Toulouse, France, 2013, p. 4.
[187] Wang, Xiao; Jin, Di; Cao, Xiaochun; Yang, Liang; Zhang, Weixiong, Semantic community identification in large attribute networks, (Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI’16 (2016), AAAI Press), 265-271
[188] Wang, Hua; Nie, Feiping; Huang, Heng; Makedon, Fillia, Fast nonnegative matrix tri-factorization for large-scale data co-clustering, (Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Volume Two. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Volume Two, IJCAI’11 (2011), AAAI Press), 1553-1558
[189] Wang, Chun; Pan, Shirui; Long, Guodong; Zhu, Xingquan; Jiang, Jing, MGAE: Marginalized graph autoencoder for graph clustering, (Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM ’17 (2017), ACM: ACM New York, NY, USA), 889-898
[190] X. Wang, L. Tang, H. Gao, H. Liu, Discovering overlapping groups in social media, in: 2010 IEEE International Conference on Data Mining, 2010, pp. 569-578.
[191] Wasserman, S.; Faust, K., Social Network Analysis: Methods and Applications (1994), Cambridge University Press
[192] Whang, J. J.; Gleich, D. F.; Dhillon, I. S., Overlapping community detection using neighborhood-inflated seed expansion, IEEE Trans. Knowl. Data Eng., 28, 5, 1272-1284 (2016)
[193] Wu, Peng; Pan, Li, Mining application-aware community organization with expanded feature subspaces from concerned attributes in social networks, Knowl.-Based Syst., 139, 1-12 (2018)
[194] Xia, Rongkai; Pan, Yan; Du, Lei; Yin, Jian, Robust multi-view spectral clustering via low-rank and sparse decomposition, (Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, AAAI’14 (2014), AAAI Press), 2149-2155
[195] Xie, Jierui; Szymanski, Boleslaw K., Towards linear time overlapping community detection in social networks, (Tan, Pang-Ning; Chawla, Sanjay; Ho, Chin Kuan; Bailey, James, Advances in Knowledge Discovery and Data Mining (2012), Springer Berlin Heidelberg: Springer Berlin Heidelberg Berlin, Heidelberg), 25-36
[196] Xu, Zhiqiang; Cheng, James; Xiao, Xiaokui; Fujimaki, Ryohei; Muraoka, Yusuke, Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering, Knowl. Inf. Syst., 53, 1, 239-268 (2017)
[197] Xu, Zhiqiang; Ke, Yiping, Effective and efficient spectral clustering on text and link data, (Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM ’16 (2016), ACM: ACM New York, NY, USA), 357-366
[198] Z. Xu, Y. Ke, Y. Wang, H. Cheng, J. Cheng, A model-based approach to attributed graph clustering, in: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, 2012, pp. 505-516.
[199] Xu, Zhiqiang; Ke, Yiping; Wang, Yi; Cheng, Hong; Cheng, James, GBAGC: A general Bayesian framework for attributed graph clustering, ACM Trans. Knowl. Discov. Data, 9, 1, 5:1-5:43 (2014)
[200] Yang, Zhao; Algesheimer, René; Tessone, Claudio J., A comparative analysis of community detection algorithms on artificial networks, Sci. Rep. (2016)
[201] Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin, Directed network community detection: A popularity and productivity link model, in: Proceedings of the 2010 SIAM International Conference on Data Mining, 2010, pp. 742-753.
[202] Yang, Tianbao; Jin, Rong; Chi, Yun; Zhu, Shenghuo, Combining link and content for community detection: A discriminative approach, (Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’09 (2009), ACM: ACM New York, NY, USA), 927-936
[203] Yang, Cheng; Liu, Zhiyuan; Zhao, Deli; Sun, Maosong; Chang, Edward Y., Network representation learning with rich text information, (Proceedings of the 24th International Conference on Artificial Intelligence. Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI’15 (2015), AAAI Press), 2111-2117
[204] Jaewon Yang, Julian J. McAuley, Jure Leskovec, Community detection in networks with node attributes, in: 2013 IEEE 13th International Conference on Data Mining, 2013, pp. 1151-1156.
[205] Ye, Wei; Zhou, Linfei; Sun, Xin; Plant, Claudia; Böhm, Christian, Attributed graph clustering with unimodal normalized cut, (Ceci, Michelangelo; Hollmén, Jaakko; Todorovski, Ljupčo; Vens, Celine; Džeroski, Sašo, Machine Learning and Knowledge Discovery in Databases (2017), Springer International Publishing: Springer International Publishing Cham), 601-616
[206] T. Yoshida, Toward finding hidden communities based on user profile, in: 2010 IEEE International Conference on Data Mining Workshops, Dec. 2010, pp. 380-387.
[207] Yu, Donghua; Liu, Guojun; Guo, Maozu; Liu, Xiaoyan, An improved k-medoids algorithm based on step increasing and optimizing medoids, Expert Syst. Appl., 92, 464-473 (2018)
[208] Zanghi, Hugo; Volant, Stevenn; Ambroise, Christophe, Clustering based on random graph model embedding vertex features, Pattern Recognit. Lett., 31, 9, 830-836 (2010)
[209] Zhang, Yuan; Levina, Elizaveta; Zhu, Ji, Community detection in networks with node features, Electron. J. Statist., 10, 2, 3153-3178 (2016) · Zbl 1359.62271
[210] Zhang, Tong; Popescul, Alexandrin; Dom, Byron, Linear prediction models with graph regularization for web-page categorization, (Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’06 (2006), ACM: ACM New York, NY, USA), 821-826
[211] Zhou, Yang; Cheng, Hong; Yu, Jeffrey Xu, Graph clustering based on structural/attribute similarities, Proc. VLDB Endow., 2, 1, 718-729 (2009)
[212] Zhou, Yang; Cheng, Hong; Yu, Jeffrey Xu, Clustering large attributed graphs: An efficient incremental approach, (Proceedings of the 2010 IEEE International Conference on Data Mining. Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM ’10 (2010), IEEE Computer Society: IEEE Computer Society Washington, DC, USA), 689-698
[213] Zhou, Ding; Manavoglu, Eren; Li, Jia; Giles, C. Lee; Zha, Hongyuan, Probabilistic models for discovering e-communities, (Proceedings of the 15th International Conference on World Wide Web. Proceedings of the 15th International Conference on World Wide Web, WWW ’06 (2006), ACM: ACM New York, NY, USA), 173-182
[214] Zhu, Shenghuo; Yu, Kai; Chi, Yun; Gong, Yihong, Combining content and link for classification using matrix factorization, (Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’07 (2007), ACM: ACM New York, NY, USA), 487-494
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.